Abstract 13640: Baseline Kidney Function and the Effects of Dapagliflozin on Health Status in Heart Failure: A Pooled Patient-Level Analysis From DFEINE-HF and PRESERVED-HF
Bibliographic record
Abstract
Background: Sodium-glucose co-transport-2 (SGLT2) inhibitors have been shown to reduce clinical events and improve health status (symptoms, function, and quality of life) in patients with HF across the range of EF. Baseline kidney dysfunction is common in HF, complicates HF management, and is strongly linked to worse health status. Aim: The study objective was to assess whether the treatment effects of dapagliflozin on health status vary based on baseline eGFR. Methods: Patient-level data were pooled from the DEFINE-HF (N = 263 participants with EF < 40%) and PRESERVED-HF trials (N = 324 participants with EF > 45%). Both were double-blind, randomized trials of dapagliflozin vs. placebo, enrolling participants with NYHA class II or higher and elevated natriuretic peptides. The primary endpoint for this analysis was Kansas City Cardiomyopathy Questionnaire Clinical Summary Score (KCCQ-CCS) at 12 weeks. Interaction of dapagliflozin effects on KCCQ-CCS by baseline eGFR (mL/min/1.73m 2 ) was assessed as categorical (i.e., eGFR <60 vs. > 60) and continuous variables. Results: Across both trials there were 583 (99.3%) participants with available baseline eGFR. The median (25 th , 75 th ) eGFR was 59 (46,77). Dapagliflozin improved KCCQ-CSS at 12 weeks (placebo-adjusted difference, +5.0 points, 95% Confidence Interval [CI] 2.6-7.5; p-value <0.001), and this was consistent in participants with an eGFR > 60 (+6.0 points, 95% CI 2.4-9.7; p=0.001) and <60 (+4.1 points, 95% CI 0.5-7.7; p=0.025) (p-interaction = 0.46). The benefits of dapagliflozin on KCCQ-CSS were robust across eGFR when modeled continuously (p-interaction = 0.46) ( Figure ). There was no heterogeneity of treatment effects when analyzing other KCCQ domains based on eGFR categorically or continuously (all p-interaction = NS). Conclusion: Treatment with dapagliflozin for 12 weeks led to significant and consistent improvements in health-related quality of life in patients with HF across a wide range of eGFRs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.024 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".